Abstract
Abstract This paper proposes an Adaptive Network-based Fuzzy Inference System (ANFIS) for short term load forecasting. The fuzzy inference system has the network structure of a neural network. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if -then rules) and stipulated input-output data pairs. The test results reveal that the ANFIS can forecast future loads with an accuracy comparable to that of neural networks, while its training is much faster than that of neural networks.
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